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Genome-wide association study identifies significant SNP and related genes associated with body size in Yorkshire pigs using latent variable modelling

Published online by Cambridge University Press:  07 September 2023

Elahe Sanjari Banestani ,
Ali Esmailizadeh ,
Mehdi Momen ,
Ahmad Ayatollahi Mehrgardi  and
Morteza Mokhtari Open the ORCID record for Morteza Mokhtari [Opens in a new window]
Elahe Sanjari Banestani
Affiliation:
Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
Ali Esmailizadeh
Affiliation:
Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
Mehdi Momen
Affiliation:
Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI 53706, USA
Ahmad Ayatollahi Mehrgardi
Affiliation:
Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
Morteza Mokhtari*
Affiliation:
Department of Animal Science, Faculty of Agriculture, University of Jiroft, P.O. Box 7867155311, Jiroft, Iran
*
Corresponding author: Morteza Mokhtari; Email: msmokhtari@ujiroft.ac.ir
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Abstract

This study aimed to quantify a latent variable for body size (BS) in pigs by using five linear body measurements including body length (BL), body height (BH), chest width (CW), chest girth (CG) and tube girth (TG), and also to identify the most associated single nucleotide polymorphisms (SNP) and related genes with BS by using the genomic best linear unbiased prediction (GBLUP) based genome-wide association study (GWAS) or GBLUP-GWAS methodology. To perform a GWAS on the BS latent trait, we used a mixed linear model and identified a total of 53 significant SNPs. Additionally, we found that nine genes, including Rho GTPase activating protein 12 (ARHGAP12), transmembrane protein 108 (TMEM108), T-cell lymphoma invasion and metastasis inducing factor 1 (TIAM1), ras homologue gene family member B (RHOB), POU class 4 homeobox 1 (POU4F1), follistatin-related protein 4 (FSTL4), cellular communication network factor 2 (CCN2), beaded filament structural protein 2 (BFSP2) and attractin-like protein 1 (ATRNL1) were associated with the BS trait in pigs. These genes are involved in several biological processes, including the regulation of anatomical structure, morphogenesis, the regulation of cell size and growth. The results suggest that the identified SNP and related genes may play important roles in regulating the growth and development of pigs. The results imply that these genes could be promising candidates for further exploration of the underlying mechanisms of body size variation. Furthermore, the findings have significant practical implications for enhancing the efficiency and profitability of pig farming through genetic selection.

Keywords

body measurementscandidate genesfactor analysisstructural equation model
Type
Animal Research Paper
Information
The Journal of Agricultural Science , Volume 161 , Issue 4 , August 2023 , pp. 599 - 605
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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References

Ba, W, Selten, MM, van der Raadt, J, van Veen, H, Li, LL, Benevento, M, Oudak er, AR, Lasabuda, RSE, Letteboer, SJ, Roepman, R, van Wezel, RJA, Courtney, MJ, van Bokhoven, H and Nadif Kasri, N (2016) ARHGAP12 functions as a developmental brake on excitatory synapse function. Cell Reports 14, 1355–1368.10.1016/j.celrep.2016.01.037CrossRefGoogle ScholarPubMed
Bentler, PM (1990) Comparative fit indexes in structural models. Psychological Bulletin 107, 238–246.CrossRefGoogle ScholarPubMed
Boyle, EA, Li, YI and Pritchard, JK (2017) An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 71177–71186.CrossRefGoogle ScholarPubMed
Browning, SR and Browning, BL (2007) Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. The American Journal of Human Genetics 81, 1084–1097.CrossRefGoogle ScholarPubMed
Cussac, D, Leblanc, P, LHeritier, A, Bertoglio, J, Lang, P, Kordon, C, Enjalbert, A and Saltarelli, D (1996) Rho proteins are localized with different membrane compartments involved in vesicular trafficking in anterior pituitary cells. Molecular and Cellular Endocrinology 119, 195–206.CrossRefGoogle ScholarPubMed
Gianola, D and Sorensen, D (2004) Quantitative genetic models for describing simultaneous and recursive relationships between phenotypes. Genetics 167, 1407–1424.CrossRefGoogle ScholarPubMed
Howe, GA and Addison, CL (2012) RhoB controls endothelial cell morphogenesis in part via negative regulation of RhoA. Vascular Cell 4, 1–11.CrossRefGoogle ScholarPubMed
Iterson, MV, van Zwet, EW and Heijmans, BT (2017) Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution. Genome Biology 18, 19.CrossRefGoogle ScholarPubMed
Kominakis, A, Hager-Theodorides, AL, Zoidis, E, Saridaki, A, Antonakos, G and Tsiamis, G (2017) Combined GWAS and ‘guilt by association-based prioritization analysis identifies functional candidate genes for body size in sheep. Genetics Selection Evolution 49, 41.CrossRefGoogle ScholarPubMed
Le, TH, Christensen, OF, Nielsen, B and Sahana, G (2017) Genome-wide association study for conformation traits in three Danish pig breeds. Genetics Selection Evolution 49, 12.CrossRefGoogle ScholarPubMed
Leal-Gutiérrez, JD, Rezende, FM, Elzo, MA, Johnson, D, Penagaricano, F and Mateescu, RG (2018) Structural equation modeling and whole-genome scans uncover chromosome regions and enriched pathways for carcass and meat quality in beef. Frontiers in Genetics 9, 1–13.CrossRefGoogle ScholarPubMed
Leguit, RJ, Raymakers, RAP, Hebeda, KM and Goldschmeding, R (2021) CCN2 (cellular communication network factor 2) in the bone marrow microenvironment, normal and malignant hematopoiesis. Journal of Cell Communication and Signaling 15, 25–56.CrossRefGoogle ScholarPubMed
Liu, H, Song, H, Jiang, Y, Jiang, Y, Zhang, F, Liu, Y, Shi, Y, Ding, X and Wang, C (2021) A single-step genome wide association study on body size traits using imputation-based whole-genome sequence data in Yorkshire pigs. Frontiers in Genetics 12, 1–14.Google ScholarPubMed
Lu, X, Mohamed Abdalla, I, Nazar, M, Fan, Y, Zhang, Z, Wu, X, Xu, T and Yang, Z (2021) Genome-wide association study on reproduction-related body-shape traits of Chinese Holstein cows. Animals 11, 1927.CrossRefGoogle ScholarPubMed
Lu, S, Hernan, R, Marcogliese, PC, Huang, Y, Gertler, TS, Akcaboy, M, Liu, S, Chung, HL, Pan, X, Sun, X, Oguz, MM, Oztoprak, U, de Baaij, JHF, Ivanisevic, J, McGinnis, E, Guillen Sacoto, MJ, Chung, WK and Bellen, HJ (2022) Loss-of-function variants in TIAM1 are associated with developmental delay, intellectual disability, and seizures. The American Journal of Human Genetics 109, 571–586.CrossRefGoogle ScholarPubMed
Maskell, LJ, Qamar, K, Babakr, AA and Hawkin, TA (2017) Essential but partially redundant roles for POU4F1/Brn-3a and POU4F2/Brn-3b transcription factors in the developing heart. Cell Death and Disease 8, 1–12.CrossRefGoogle ScholarPubMed
McLaren, W, Gil, L, Hunt, SE, Riat, HS, Ritchie, GRS, Thormann, A, Flicek, P and Cunningham, F (2016) The ensembl variant effect predictor. Genome Biology 17, 122.CrossRefGoogle ScholarPubMed
Meyer, K (2007) WOMBAT – a tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). Journal of Zhejiang University Science B 8, 815–821.CrossRefGoogle ScholarPubMed
Meyer, K and Tier, B (2012) “snp snappy”: a strategy for fast genome-wide association studies fitting a full mixed model. Genetics 190, 275–277.CrossRefGoogle Scholar
Momen, M, Bhatta, M, Hussain, W, Yu, H and Morota, G (2021) Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning. Plant Direct 5, e00304.CrossRefGoogle ScholarPubMed
Neale, BM, Medland, SE, Ripke, S, Asherson, P, Franke, B, Lesch, K-P, Faraone, SV, Nguyen, TT, Schafer, H, Holmans, P, Daly, M, Steinhausen, H-C, Freitag, C, Reif, A, Renner, TJ, Romanos, M, Romanos, J, Walitza, S, Warnke, A, Meyer, J, Palmason, H, Buitelaar, J, Vasquez, AA, Lambregts-Rommelse, N, Gill, M, Anney, RJL, Langely, K, O'Donovan, M, Williams, N, Owen, M, Thapar, A, Kent, L, Sergeant, J, Roeyers, H, Mick, E, Biederman, J, Doyle, A, Smalley, S, Loo, S, Hakonarson, H, Elia, J, Todorov, A, Miranda, A, Mulas, F, Ebstein, RP, Rothenberger, A, Banaschewski, T, Oades, RD, Sonuga-Barke, E, McGough, J, Nisenbaum, L, Middleton, F, Hu, X and Nelson, S (2010) Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry 49, 884–897.CrossRefGoogle ScholarPubMed
Niu, P, Kim, S, Choi, B, Kim, T, Kim, J and Kim, K (2013) Porcine insulinlike growth factor 1 (IGF1) gene polymorphisms are associated with body size variation. Genes and Genomics 35, 523–528.CrossRefGoogle Scholar
Ohnishi, C and Satoh, M (2018) Estimation of genetic parameters for performance and body measurement traits in Duroc pigs selected for average daily gain, loin muscle area, and backfat thickness. Livestock Science 214, 161–166.CrossRefGoogle Scholar
Purcell, S, Neale, B, Todd-Brown, K, Thomas, L, Ferreira, MA, Bender, D, Maller, J, Sklar, P, De Bakker, PI, Daly, MJ and Sham, PC (2007) Plink: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics 81, 559–575.CrossRefGoogle ScholarPubMed
R Development Core Team (2021) R: A Language and Environment for Statistical Computing. Vienna, Austria: R foundation for statistical computing.Google Scholar
Rebolledo, DL, Acuna, MJ and Brandan, E (2021) Role of matricellular CCN proteins in skeletal muscle: focus on CCN2/CTGF and its regulation by vasoactive peptides. International Journal of Molecular Sciences 22, 5234.CrossRefGoogle ScholarPubMed
Rodrigues-Diez, RR, Tejera-Munoz, A, Esteban, V, Steffensen, LB, Rodrigues-Díez, R, Orejudo, M, Rayego-Mateos, S, Falke, LL, Cannata-Ortiz, P, Ortiz, A, Egido, J, Mallat, Z, Briones, AM, Auxiliadora Bajo, M, Goldschmeding, R and Ruiz-Ortega, M (2022) CCN2 (cellular communication network factor 2) deletion alters vascular integrity and function predisposing to aneurysm formation. Hypertension 79, 42–55.CrossRefGoogle ScholarPubMed
Rosa, GJM, Valente, BD, de los Campos, G, Wu, XL, Gianola, D and Silva, MA (2011) Inferring causal phenotype networks using structural equation models. Genetics Selection Evolution 43, 6.CrossRefGoogle ScholarPubMed
Rosseel, Y (2012) lavaan: an R package for structural equation modeling. Journal of Statistical Software 48, 1–36.CrossRefGoogle Scholar
Schumacker, ER and Lomax, GR (1996) A Beginner's Guide to Structural Equation Modeling. Mahwah, NJ: Erlbaum.Google Scholar
Silva, HT, Paiva, JT, Botelho, ME, Carrara, ER, Lopes, PS, Silva, FF, Veroneze, R, Sterman Ferraz, JB, Eler, JP, Mattos, EC and Gaya, LG (2021) Searching for causal relationships among latent variables concerning performance, carcass, and meat quality traits in broilers. Journal of Animal Breeding and Genetics 139, 181–192.CrossRefGoogle ScholarPubMed
Song, S, Landsbury, A, Dahm, R, Liu, Y, Zhang, Q and Quinlan, RA (2009) Functions of the intermediate filament cytoskeleton in the eye lens. The Journal of Clinical Investigation 119, 1837–1848.CrossRefGoogle ScholarPubMed
Song, H, Zhang, J, Zhang, Q and Ding, X (2019) Using different single-step strategies to improve the efficiency of genomic prediction on body measurement traits in pig. Frontiers in Genetics 9, 1–10.CrossRefGoogle ScholarPubMed
Stark, Z, Bruno, DL, Mountford, H, Lockhart, PJ and Amor, DJ (2010) De novo 325 kb microdeletion in chromosome band 10q25.3 including ATRNL1 in a boy with cognitive impairment, autism and dysmorphic features. European Journal of Medical Genetics 53, 337–339.CrossRefGoogle Scholar
Steiger, JH (1990) Structural model evaluation and modification: an interval estimation approach. Multivariate Behavioral Research 25, 173–180.CrossRefGoogle ScholarPubMed
Turner, SD (2018) qqman: an R package for visualizing GWAS results using Q-Q and Manhattan plots. The Journal of Open Source Software 3, 1–2.CrossRefGoogle Scholar
VanRaden, PM (2008) Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.CrossRefGoogle ScholarPubMed
Vega, FM and Ridley, AJ (2018) The RhoB small GTPase in physiology and disease. Small GTPases 9, 384–393.CrossRefGoogle ScholarPubMed
Wang, H, Wang, X, Li, M, Sun, H, Chen, Q, Yan, D, Dong, X, Pan, Y and Lu, S (2022) Genome-wide association study of growth traits in a four-way crossbred pig population. Genes 13, 1990.CrossRefGoogle Scholar
Weller, JI, Song, JZ, Heyen, DW, Lewi, HA and Ron, M (1998) A new approach to the problem of multiple comparisons in the genetic dissection of complex traits. Genetics 150, 1699–1706.CrossRefGoogle Scholar
Wright, S (1921) Correlation and causation. Journal of Agricultural Research 20, 557–585.Google Scholar
Xu, Z, Ji, C, Zhang, Y, Zhang, Z, Nie, Q, Xu, J, Zhang, D and Zhang, X (2016) Combination analysis of genome-wide association and transcriptome sequencing of residual feed intake in quality chickens. BMC Genomics 17, 594.CrossRefGoogle ScholarPubMed
Yu, H, Campbell, MT, Zhang, Q, Walia, H and Morota, G (2019) Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes. Genes. Genomes Genetics 9, 1975–1986.Google Scholar
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